Beyond demonstrating that FoldingDiff's generated backbones contain reasonable structural motifs, it is also important to show that they are designable – meaning that we can find a sequence of amino acids that can fold into the designed backbone structure. After all, a novel protein structure is not useful if we cannot physically realize it. Previous works evaluate this in silico by predicting possible amino acids that fold into a generated backbone and checking whether the predicted structure for these sequences matches the original backbone. Following this general procedure, for a generated structure \(s\) , we use the ESM-IF1 inverse folding model [1]} to generate 8 different amino acid sequences. We then use OmegaFold [2]} to predict the 3D structures \(\hat{s}_1,\ldots ,\hat{s}_8\)  corresponding to each of these sequences. We use TMalign [3]}, which evaluates structural similarity between backbones, to score each of these 8 structures against the original structure \(s\) . The maximum score \(\max _{i\in [1, 8]} \mathrm {TMalign}(s, \hat{s}_i)\)  is the self-consistency TM (scTM) score. A scTM score of \(\ge \)  0.5 is considered to be in the same fold, and thus is “designable.” We repeat this process for each of our 780 generated backbones. This evaluation is similar to previous evaluations done by [4]} and [5]}, except that we use OmegaFold instead of AlphaFold [6]}. OmegaFold is designed to be used without multiple sequence alignments (MSAs), and performs similarly to AlphaFold while generalizing better to orphan proteins that may not have such evoluationary annotations [2]}. Furthermore, given that prior works use AlphaFold without MSA information in their evaluation pipelines [4]}, [5]}, OmegaFold appears to be a more appropriate method for scTM evaluation.
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